Why visualize data?

The greatest value of a picture is when it forces us to notice what we never expected to see.

• Tukey (1977) quoted in Yau (2013)

Exploring data visually

Our schedule:

• Current activities: data exploration through visualization with common chart types
• Weeks 10-15: deep dive into data visualization
  • More complex chart types
  • How to customize your seaborn plots
  • Best practices in data visualization
  • Interactive web-based graphics
  • Maps!

Exploratory chart types

• Comparing categories: bar chart, dot plot
• Part-to-whole: pie chart
• Change over time: line chart
• Connections and relationships: scatter plot

Many, many more in these categories - these are just our focus for today!

Python and the web

• A brief aside: With Python, data on the web is at your fingertips (our topic for Week 9)
• This week, you will get a preview

import pandas as pd

mx_csv = 'http://personal.tcu.edu/kylewalker/mexico.csv'
mx = pd.read_csv(mx_csv)
mx.head()

Comparing categories

How about sorting our data?

mx_sorted = mx.sort('gdp08', ascending = False)
mx_sorted.head()

Bar charts

Bar charts

• Length or height of bars proportional to data values, allowing for comparisons between categories
• The value axis of bar charts must start at zero!!!
• Recommendation: sort your data values for ease of interpretation

Bar chart with non-zero origin

Bar charts in Python

%matplotlib inline
import seaborn as sns

mx.plot(x= 'name', y = 'gdp08', kind = 'bar')

Bar charts in seaborn

sns.set(style = 'whitegrid')
sns.barplot(x = 'gdp08', y = 'name', data = mx_sorted)

Dot plots

Dot plots

• Can be preferable to bar charts - values determined by position along axis rather than bar heights
• In turn, zero origin not strictly necessary (though consider the context)
• Sorted data also preferable for dot plots

Dot plots in seaborn

sns.stripplot(x = 'gdp08', y = 'name', data = mx_sorted)

Part-to-whole

• Categories in relationship to the entire population of values
• Examples: pie chart, waffle chart, 100% bar chart, tree map
• Must sum to 100%!

Pie charts in Python

zac = mx[mx.name == 'Zacatecas'].drop(['name', 'FID', 'gdp08', 'mus09'], axis = 1).squeeze()
zac.name = 'Zacatecas'
zac.plot(kind = 'pie', figsize = (6, 6))

Problems with pie charts

Problems with pie charts

Line charts

Line charts in pandas

hs_drop = pd.read_csv('http://personal.tcu.edu/kylewalker/data/hs_drop.csv')
hs_drop.sort('year', inplace = True)
hs_drop.set_index('year', inplace = True)
hs_drop.plot() # pandas plotting defaults to line charts, infers x from index

Line charts in seaborn

• Connected points available in pointplot and factorplot
• Requires long-form data! (More to come on this in the next two weeks)

hs_drop.reset_index(inplace = True)
hs_long = pd.melt(hs_drop, id_vars = 'year', 
                  value_vars = ['m_rate', 'f_rate'], 
                  value_name = 'percent_drop', var_name = 'gender')
# We use factorplot because it gives us greater control over the axes
chart = sns.factorplot(data = hs_long, x = 'year', 
                       y = 'percent_drop', hue = 'gender', size = 8)
chart.set_xticklabels(rotation = 45, step = 3)

Line charts in seaborn

Scatter plots

• Question: how do the values in two columns covary?
  
• Scatter plot: each observation represented by a point; position along x axis dictated by one column value; position along y axis dictated by other column value
• Regression line: visual representation of estimated statistical relationship between X and Y

Scatter plots

Scatter plots in pandas

mx.plot(x = 'mus09', y = 'pri10', kind = 'scatter')

Scatter plots in seaborn

• Available in the lmplot and regplot functions

sns.lmplot(data = mx, x = 'mus09', y = 'pri10')

Correlation

• Correlation coefficient: statistical representation of how two samples covary; ranges between -1 (negative correlation) and +1 (positive correlation)
• In pandas: .corr()
• Beware of spurious correlations! http://tylervigen.com/spurious-correlations

mx['mus09'].corr(mx['pri10'])

0.41639990565936902 # the result